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University of Cambridge > Talks.cam > Computational Neuroscience > Computational Neuroscience Journal Club
Computational Neuroscience Journal ClubAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Jake Stroud. Please join us for our fortnightly journal club online via zoom where two presenters will jointly present a topic together. Zoom information: https://us02web.zoom.us/j/84612479602?pwd=RVMrbjRLWVlBM2pndE9VbWVJWWlGZz09 Meeting ID: 846 1247 9602 Passcode: 552142 The next topic is ‘Hippocampal/entorhinal representations of non-spatial maps’: The hippocampal formation has long been known to encode two-dimensional space using a representation of place cells in the hippocampus and grid cells in entorhinal cortex. However, recent experimental and theoretical work has suggested that these regions can also represent non-spatial maps and variables. We will start by giving a brief overview of place and grid cells and how they encode space. We will then proceed to discuss extensions to high-dimensional and non-spatial variables, focusing on the following three papers: Efficient and flexible representation of higher-dimensional cognitive variables with grid cells. Klukas et al., PLOS Computational Biology (2020). Mapping of a non-spatial dimension by the hippocampal-entorhinal circuit. Aronov et al., Nature (2017). The Tolman-Eichenbaum Machine: Unifying space and relational memory through generalisation in the hippocampal formation. Whittington et al., bioRxiv (2019). Additional background reading for those interested: What Grid Cells Convey about Rat Location. Fiete et al., Journal of Neuroscience (2008). Organizing conceptual knowledge in humans with a gridlike code. Constantinescu et al., Science (2016). This talk is part of the Computational Neuroscience series. This talk is included in these lists:
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